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The world is becoming more data-dependent, which means intolerance for network downtime—or even noticeable lag—is on the rise. Unlike in earlier periods of the digital age, poor network performance is not just an annoyance; it is now a threat to our productivity, our lifestyles and perhaps even our lives.
At the same time, network infrastructure is evolving into a multi-party construct in which one data stream interacts with dozens of independent providers, any one of which could become the weak link between application and user. This is forcing digital organizations to become more proactive in their network monitoring and management, which is driving demand for increasingly sophisticated, and intelligent, analytics engines.
Operational Insight
It’s a basic tenet of networking that you cannot manage what you cannot see or understand. This is why many organizations are turning to new generations of artificial intelligence (AI)-powered analytics, which can not only crunch performance data faster and more accurately than current software but can also dynamically adjust their focus to detect anomalies and data patterns that would otherwise remain hidden. (Also read: How Artificial Intelligence Will Revolutionize the Sales Industry.)
According to 360 Market Updates, the global market for network analytics is on pace to more than double, to $2.7 billion, by 2026—a compound annual growth rate of 16.4%. The key takeaway is that modern analytics does more than just monitor bit rates and throughput—it encompasses a wide range of metrics to ensure that networks are not just functional but optimized. As well, intelligent analytics can evolve dynamically, just as data patterns do, meaning they can keep pace with new deployments and new use cases without programmers' or network operators' direct control.
Intelligent analytics' ability to evolve will prove crucial as organizations undergo the digital transformation that will place much of the enterprise data ecosystem under intelligent control, says Enterprise Networking Planet’s Michael Sumastre. In a digital economy, we can expect the pace of business to accelerate rapidly—even as profit margins become narrower and opportunities emerge from highly targeted, segmented markets. This means things like network resource usage, load balancing and a host of other functions must jump to near-real-time to ensure data and services can be leveraged for maximum benefit. With 5G networks and the Internet of Things (IoT) connecting everything from cars to health monitoring devices, performance degradation will become far more serious than a few seconds of lag as you’re streaming the latest cat video. (Also read: Big Data and 5G: Where Does This Intersection Lead?)
Equally important is the ability to reduce the current cost and complexity of network management. As network automation vendor Accedian points out, performance monitoring and troubleshooting are major cost centers for network providers, not only in terms of day-to-day operations but in lost revenues due to outages. By deploying intelligent agents throughout network infrastructure, however, organizations can quickly ascertain the root of any problem, shift traffic around the affected systems and then effect repairs at a much faster rate than a traditional management environment. And even this level of corrective action will become rare because the intelligence infused throughout the network will be able to identify small problems long before they become big problems, meaning the fix can be implemented before the user even knows there is a problem.
Honest Networking
AI does not improve network performance on a purely operational level. It can also delve into traffic patterns and other data sets to ensure networks are used for their intended purpose and to protect against hacking and data theft. Security firm Cylynx recently outlined a number of ways in which financial institutions, insurance companies and other organizations are using intelligent analytics to combat theft, fraud and abuse of their networks—a problem estimated to cost upwards of $5 trillion per year, nearly 6% of the global GDP. (Also read: How Cybercriminals Use GDPR as Leverage To Extort Companies.)
Through massive data gathering and high-speed intelligent analytics, organizations can spot the patterns revealing all manner of scams, including fraud rings conducting identity theft, forgery and other crimes, as well as attempts to create fake IDs, take over accounts and submit false information to acquire funds. In addition, many of these patterns also contain digital clues allowing investigators to track down the perpetrators.
Conclusion
While AI is being introduced to enterprise data environments in a number of settings, its deployment is still at the very initial stages. As yet, most use cases are still in the test phase—because no one is quite sure what AI will do given the opportunity.
However, it does seem likely that the more AI infiltrates the digital world, the more it will be relied upon to maintain the myriad intricate balances necessary for a smooth-functioning environment. And nowhere will this be more profound than the network.
